Drone Collision Prevention System Optimization Techniques

Controlling unmanned vehicles to prevent collisions and reduce user burden when multiple vehicles are operated. It provides autonomous control for commercial off-the-shelf unmanned vehicles via an add-on controller that receives user inputs and generates modified control signals to instruct the vehicles to follow pre-determined routes. The controller analyzes the user inputs and extracts the intended maneuvering commands while discarding velocity changes. To avoid collisions, the routes are generated by a server based on sensor data and deconflicting with other vehicles.

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Machine learning can be used to guide unmanned aerial vehicles (UAVs) and other platforms around obstacles without expensive imaging systems. The approach involves training ML models to generate guidance information like object locations based on time-of-arrival (TOA) data from sensors. This avoids the computational expense of processing images to identify obstacles in real-time on board the platform.

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Flight control system for unmanned aerial vehicles that enables safe passing of nearby aircraft. The system detects nearby aircraft and determines if passing is possible based on their trajectories and airspace conditions. If passing is possible, it controls the drone to perform a passing maneuver at a safe distance from the other aircraft.

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System for mid-air collision avoidance and traffic control between unmanned aerial platforms with different priority levels. The system involves CAS (collision avoidance system) units on each platform that intermittently transmit their locations. Higher-priority platforms can receive these transmissions and calculate collision risks. If the risk is high, the higher priority platform CAS unit generates and transmits steering commands to the lower priority platform to avoid a collision.

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A recovery system for unmanned aerial vehicles (UAVs) to prevent catastrophic crashes and damage when a UAV experiences critical failures. The recovery system has a parachute housed in an ejectable pod mounted on the UAV. Sensors monitor flight status and triggers like loss of power or communication. When a critical failure is detected, the pod is ejected, and the parachute deploys to slow the UAV's descent and prevent crashing.

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A drone control system that prevents collisions and enables filming subjects from different angles. The controller uses predefined rails of positions for the drone to follow, along with mathematical modeling. The rails are virtual paths that prevent collisions and give coordinated shots. The drone minimizes a cost function that includes variables like contour error and lag error to optimize rail following.

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Collision avoidance for vehicles like unmanned aerial vehicles (UAVs) using modulated light beacons from objects like buildings. The buildings emit light beacons modulated with useful signals carrying object information. UAVs have event-based vision sensors that detect changes in beacon brightness and estimate distances to avoid collisions. This enables collision avoidance without external infrastructure or RF systems.

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Detecting obstacles and targets using passive infrared sensors on drones and vehicles to enable efficient collision avoidance and tracking. PIR sensors receive heat signals from obstacles like humans, calculate distances, and determine whether to take collision avoidance maneuvers. The PIR signals also allow for the recognition of targets like humans for tracking.

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Dynamic path planning allows vehicles like aircraft to safely navigate an area with obstacles and other vehicles. It models the area as sub-areas and calculates a summation value for each sub-area based on proximity to obstacles. Sub-areas meeting a rule are connected to form the vehicle's path through the area. As obstacles move or new ones appear, the path is recomputed in real time to avoid them.

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A drone with flexible arms that can be selectively bent using integrated actuators. The arms are made of lightweight, semi-rigid materials like inflatable structures. They have electromagnetic actuator cells that can bend the arms when activated. The drone's control system can selectively activate the actuators to flex the arms in different configurations. This allows the drone to reposition its rotors to avoid obstacles and crashes. The flexible arm design enhances maneuverability and crash resilience compared to rigid drone arms.

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Using crowdsourced obstacle data to improve the flight safety and efficiency of unmanned aerial vehicles (UAVs). The system involves a UAV management device that receives obstacle data from various sources, such as crowdsourcing, trusted authorities, third-party repositories, and onboard UAV sensors. It uses this data to adjust flight plans in real time, avoiding obstacles and reducing collision risks. The device maintains a repository of static and dynamic obstacles to improve routing recommendations for UAVs.

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Method for avoiding collision of unmanned aerial vehicles in crowded areas, like cities, where obstacles are densely packed. The method uses potential fields that calculate attractive forces from a target point and repulsive forces from obstacles. The potential fields are computed based on vehicle position and obstacle proximity. The resulting force vector is used to determine a collision avoidance direction. The vehicle maneuvers to avoid collisions by moving in a direction opposite to the potential force vector. In downtown areas with dense obstacles, multiple potential fields are calculated that account for obstacle proximity and relative velocity.

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A drone that can avoid obstacles and collisions by using predefined virtual rail paths to guide flight. The rail paths are stored in memory, and the drone controller approximates their position on the rail. The controller then minimizes a cost function that includes lag and contour errors to stay on the rail while avoiding obstacles. The rail paths can be modified, and drones with cameras can track subjects on the rail. The rail guidance allows safer flight in complex environments compared to manual control.

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A drone landing system that prevents collisions and enables controlled takeoff/landing speeds and wireless charging. The landing stand has a retractable extension that passes through a hole in the drone's body. This generates an eddy current and magnetic braking to slow the drone's descent and prevent crashes. It also allows speed control during takeoff. The extension can also charge the drone wirelessly while docked. The landing system can be added as a detachable module for existing drones without modifying their bodies.

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A collision avoidance system for manned aircraft to detect and avoid drones in their flight path. The system uses onboard cameras and image processing to identify drones ahead of the aircraft. It then sends a signal to disrupt the drone's control systems, forcing it to move out of the way. The system also alerts the pilot about the drone's presence so they can take evasive action if needed.

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Collision avoidance system for drones that allows multiple drones to safely operate in environments containing numerous obstructions, without the need for direct communication or coordination between the drones. The system uses cameras on the drones to capture images projected by other drones. These images are encoded with data about the projecting drone's trajectory, speed, location, etc. The capturing drone decodes this data to determine the projecting drone's trajectory and alters its own flight path to avoid colliding.

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Preventing collisions between drones using drone-to-drone acoustic communications. Drones track their routes but also detect and exchange acoustic signals to determine updated routes that avoid collisions. Acoustic sensors on drones receive signals from nearby drones and a collision prevention engine analyzes them to calculate modified routes. This enables real-time collision avoidance without relying on external infrastructure or onboard sensors. Drones also broadcast their own signals to facilitate acoustic communication.

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Collision avoidance for unmanned aerial vehicles (UAVs) that enables safe, autonomous flight of multiple drones in close proximity using cameras. The collision avoidance system uses pan/tilt cameras to capture images of the ground ahead. It then matches visual features in those images with features received from neighboring drones. By transforming the neighbor's feature locations into its own reference frame, a drone can determine if a collision will occur based on the neighbor's trajectory. If a collision is predicted, the drone can update its own trajectory to avoid it. This allows drones to detect and avoid collisions with other drones using only cameras and shared visual information.

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Unmanned aerial vehicle (UAV) that can autonomously avoid collisions when multiple UAVs are flying in close proximity. The UAV uses a laser light source and camera to detect and track other nearby UAVs. By analyzing the captured images, the UAV can measure the positions of other UAVs and determine if there is a risk of collision. If so, it will autonomously maneuver to avoid the other UAVs.

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System for remote drone navigation through airspace with reduced risk of collision by blocking flight paths through high risk zones on a flight risk map. The system evaluates drone flight paths against a map of airspace zones scored for safety. If a flight path exceeds an acceptable risk threshold, it is blocked. The system can also take over drone navigation to avoid high risk zones.

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